Move iiasafy functions to output.R

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: iamrpt
 Title: Convert GCAM results to the format required by various IAM experiment databases
-Version: 0.1.0-9000
+Version: 0.2.0
 Authors@R: c(
     person("Robert", "Link", email = "[email protected]", role = c("aut", "cre")),
     person("Xavier", "Gutierrez", email = "[email protected]", role = c("aut"))

R/mcl.R

@@ -375,57 +375,3 @@ validate1 <- function(ctl, ctlname, expectcols, rqdcols) {
     }
 }
 
-#' Convert a list of tables to a single table in IIASA format
-#'
-#' The result of this transformation will be a single table with the following
-#' columns:
-#'
-#' \itemize{
-#'   \item{Model}
-#'   \item{Scenario}
-#'   \item{Region}
-#'   \item{Variable (taken from the output name of the input)}
-#'   \item{Unit}
-#'   \item{NNNN - one for each year}
-#' }
-#'
-#' @param datalist List of data frames, one for each variable.
-#' @keywords internal
-iiasafy <- function(datalist)
-{
-    varlist <- lapply(datalist, proc_var_iiasa)
-
-    varlist <- lapply(names(varlist),   # Add variable name (need access to names(varlist) for this.)
-                      function(var) {
-                          dplyr::mutate(varlist[[var]], Variable=var)
-                      }) %>%
-      dplyr::bind_rows()              # Combine into a single table
-}
-
-
-#' Select the columns needed for the IIASA format
-#'
-#' Starting with data in long format, keep only the columns needed to form the
-#' IIASA format, namely, scenario, region, year, value, and Units.  Then rename
-#' variables according to the IIASA conventions, and spread to wide format.  We don't
-#' add the model or variable names at this point, however.
-#' @keywords internal
-proc_var_iiasa <- function(df)
-{
-    scenario <- region <- variable <- year <- value <- Units <- NULL # silence
-                                        # check notes
-    df <- df %>%
-        dplyr::select(scenario, region, year, value, Units) %>%
-        dplyr::rename(Scenario=scenario, Region=region, Unit=Units) %>%
-        tidyr::spread(year, value)
-}
-
-#' Put columns in canonical order for IIASA data format
-#'
-#' @param df Data frame
-#' @keywords internal
-iiasa_sortcols <- function(df)
-{
-    cols <- unique(c('Model', 'Scenario', 'Region', 'Variable', 'Unit', names(df)))
-    dplyr::select(df, dplyr::one_of(cols))
-}

R/output.R

@@ -124,3 +124,59 @@ nameparse <- function(name)
         c(stringr::str_c(splt[1:(len-1)], collapse='.'), splt[len])
     }
 }
+
+
+#' Convert a list of tables to a single table in IIASA format
+#'
+#' The result of this transformation will be a single table with the following
+#' columns:
+#'
+#' \itemize{
+#'   \item{Model}
+#'   \item{Scenario}
+#'   \item{Region}
+#'   \item{Variable (taken from the output name of the input)}
+#'   \item{Unit}
+#'   \item{NNNN - one for each year}
+#' }
+#'
+#' @param datalist List of data frames, one for each variable.
+#' @keywords internal
+iiasafy <- function(datalist)
+{
+    varlist <- lapply(datalist, proc_var_iiasa)
+
+    varlist <- lapply(names(varlist),   # Add variable name (need access to names(varlist) for this.)
+                      function(var) {
+                          dplyr::mutate(varlist[[var]], Variable=var)
+                      }) %>%
+      dplyr::bind_rows()              # Combine into a single table
+}
+
+
+#' Select the columns needed for the IIASA format
+#'
+#' Starting with data in long format, keep only the columns needed to form the
+#' IIASA format, namely, scenario, region, year, value, and Units.  Then rename
+#' variables according to the IIASA conventions, and spread to wide format.  We don't
+#' add the model or variable names at this point, however.
+#' @keywords internal
+proc_var_iiasa <- function(df)
+{
+    scenario <- region <- year <- value <- Units <- NULL # silence
+                                        # check notes
+    df <- df %>%
+        dplyr::select(scenario, region, year, value, Units) %>%
+        dplyr::rename(Scenario=scenario, Region=region, Unit=Units) %>%
+        tidyr::spread(year, value)
+}
+
+#' Put columns in canonical order for IIASA data format
+#'
+#' @param df Data frame
+#' @keywords internal
+iiasa_sortcols <- function(df)
+{
+    cols <- unique(c('Model', 'Scenario', 'Region', 'Variable', 'Unit', names(df)))
+    dplyr::select(df, dplyr::one_of(cols))
+}

tests/testthat/test_output.R

@@ -189,3 +189,37 @@ test_that('output_csv works for single tab mode.',
                      'billion m^2,Reference,Africa_Northern,resid,resid,resid_building,2075,5.3585')
 
 })
+
+
+test_that('Single table can be converted to iiasa format.', {
+    pop <- dplyr::filter(popq, year >= 2005, year <= 2020)
+    popiia <- proc_var_iiasa(pop) %>%
+      dplyr:::mutate(Variable='Population', Model='GCAM') %>%
+      iiasa_sortcols()
+
+    expect_equal(nrow(popiia), length(unique(pop$region)))
+    expect_identical(names(popiia), c('Model', 'Scenario', 'Region', 'Variable',
+                                      'Unit', as.character(seq(2005,2020,5))))
+    expect_equal(popiia[['2010']], dplyr::filter(pop, year==2010)[['value']])
+})
+
+test_that('List of tables can be converted to iiasa format.', {
+    pop <- dplyr::filter(popq, year >= 2005, year <= 2020)
+    flrspc <-
+        dplyr::filter(flrspcq, year >= 2005, year <= 2020) %>%
+          aggregate('sum', 'scenario, region')
+
+    allvar <- list(Population=pop, Floorspace=flrspc)
+
+    iitbl <- iiasafy(allvar) %>%
+      dplyr::mutate(Model='GCAM') %>%
+      iiasa_sortcols()
+
+    expect_true(is.data.frame(iitbl))
+    expect_equal(nrow(iitbl), 2*length(unique(pop$region)))
+    expect_identical(names(iitbl), c('Model', 'Scenario', 'Region', 'Variable',
+                                     'Unit', as.character(seq(2005,2020,5))))
+
+    expect_identical(unique(iitbl$Variable), c('Population', 'Floorspace'))
+
+})